{ "cells": [ { "cell_type": "markdown", "id": "65fe6a4e-f751-40c3-80ed-12c66e728784", "metadata": {}, "source": [ "# Temperature Salinity Diagram" ] }, { "cell_type": "markdown", "id": "35b13488-c6b8-40be-8432-3df28b390664", "metadata": {}, "source": [ "This notebook shows how to plot a temperature-salinity diagram which is weighted by volume using xhistogram. \n", "\n", "Output from both MOM5 or MOM6 can be used.\n", "\n", "**Requirements**: The conda/analysis3 (or later) module on ARE. A session with 4 cores is sufficient for this example but more cores will be needed for larger datasets. \n", "\n", "Firstly, we load all required modules and start a client." ] }, { "cell_type": "code", "execution_count": 1, "id": "14189095-05e3-4e8f-b5dc-648498e5b783", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import xarray as xr\n", "import numpy as np\n", "import cosima_cookbook as cc\n", "from dask.distributed import Client\n", "import gsw\n", "from xhistogram.xarray import histogram as xhistogram\n", "import cf_xarray as cfxr\n", "import warnings\n", "warnings.simplefilter('ignore')\n", "\n", "## plotting\n", "import matplotlib.pyplot as plt\n", "import cmocean.cm as cmo\n", "import matplotlib.colors as colors" ] }, { "cell_type": "code", "execution_count": 2, "id": "96030807-8749-4e3f-83b8-72d625e92c8e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "
\n", "
\n", "

Client

\n", "

Client-646264f3-a062-11ed-a4cd-00000772fe80

\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "
Connection method: Cluster objectCluster type: distributed.LocalCluster
\n", " Dashboard: /proxy/39221/status\n", "
\n", "\n", " \n", "\n", " \n", "
\n", "

Cluster Info

\n", "
\n", "
\n", "
\n", "
\n", "

LocalCluster

\n", "

817f5334

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", " \n", "
\n", " Dashboard: /proxy/39221/status\n", " \n", " Workers: 7\n", "
\n", " Total threads: 28\n", " \n", " Total memory: 112.00 GiB\n", "
Status: runningUsing processes: True
\n", "\n", "
\n", " \n", "

Scheduler Info

\n", "
\n", "\n", "
\n", "
\n", "
\n", "
\n", "

Scheduler

\n", "

Scheduler-3c8dbc89-bb9d-4941-9ec9-6ddc23628db0

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", " Comm: tcp://127.0.0.1:46113\n", " \n", " Workers: 7\n", "
\n", " Dashboard: /proxy/39221/status\n", " \n", " Total threads: 28\n", "
\n", " Started: Just now\n", " \n", " Total memory: 112.00 GiB\n", "
\n", "
\n", "
\n", "\n", "
\n", " \n", "

Workers

\n", "
\n", "\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 0

\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:45779\n", " \n", " Total threads: 4\n", "
\n", " Dashboard: /proxy/40195/status\n", " \n", " Memory: 16.00 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:33593\n", "
\n", " Local directory: /jobfs/71077001.gadi-pbs/dask-worker-space/worker-3qr7ppqe\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 1

\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:37439\n", " \n", " Total threads: 4\n", "
\n", " Dashboard: /proxy/37985/status\n", " \n", " Memory: 16.00 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:42647\n", "
\n", " Local directory: /jobfs/71077001.gadi-pbs/dask-worker-space/worker-0cget0n8\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 2

\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:36971\n", " \n", " Total threads: 4\n", "
\n", " Dashboard: /proxy/45137/status\n", " \n", " Memory: 16.00 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:44953\n", "
\n", " Local directory: /jobfs/71077001.gadi-pbs/dask-worker-space/worker-hopukhfh\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 3

\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:38489\n", " \n", " Total threads: 4\n", "
\n", " Dashboard: /proxy/32955/status\n", " \n", " Memory: 16.00 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:44723\n", "
\n", " Local directory: /jobfs/71077001.gadi-pbs/dask-worker-space/worker-befoobp0\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 4

\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:46429\n", " \n", " Total threads: 4\n", "
\n", " Dashboard: /proxy/35921/status\n", " \n", " Memory: 16.00 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:43179\n", "
\n", " Local directory: /jobfs/71077001.gadi-pbs/dask-worker-space/worker-o9u5k7k2\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 5

\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:44453\n", " \n", " Total threads: 4\n", "
\n", " Dashboard: /proxy/38357/status\n", " \n", " Memory: 16.00 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:39165\n", "
\n", " Local directory: /jobfs/71077001.gadi-pbs/dask-worker-space/worker-sb4zzmr5\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 6

\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:33031\n", " \n", " Total threads: 4\n", "
\n", " Dashboard: /proxy/40619/status\n", " \n", " Memory: 16.00 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:40605\n", "
\n", " Local directory: /jobfs/71077001.gadi-pbs/dask-worker-space/worker-ob69v54g\n", "
\n", "
\n", "
\n", "
\n", " \n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "
\n", "
\n", " \n", "\n", "
\n", "
" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "client = Client()\n", "client" ] }, { "cell_type": "markdown", "id": "fc82535c-a04a-43d4-8703-73a0d96270bf", "metadata": { "tags": [] }, "source": [ "## Load data\n", "\n", "Select output from MOM5 or MOM6 and coordinates of meridional section." ] }, { "cell_type": "code", "execution_count": 3, "id": "acf894b9-0a96-4e1e-80e2-c08d8233a9c0", "metadata": {}, "outputs": [], "source": [ "lon = -25 # longitude of meridional section\n", "lat = slice(-90, -37) # latitude range of section\n", "\n", "session = cc.database.create_session()" ] }, { "cell_type": "markdown", "id": "899d0501-ed19-49ca-bfe0-b21bd8528eb2", "metadata": {}, "source": [ "Next, choose an experiment of any resolution. Here, only 1 year is selected; if you want to use a longer time period, you might need more resources!" ] }, { "cell_type": "code", "execution_count": 4, "id": "b3fc0fe0-7b35-49eb-b18a-c34819a59a6d", "metadata": {}, "outputs": [], "source": [ "# dictionary of experiment name and time for mom5 and mom6\n", "expt_args = {\n", " \"mom5\": {\n", " \"expt\": \"01deg_jra55v13_ryf9091\",\n", " \"start_time\": \"1991-01-01\",\n", " \"end_time\": \"1991-12-31\"},\n", " \"mom6\": {\n", " \"expt\": \"panant-01-zstar-v13\",\n", " \"start_time\": \"1991-01-01\",\n", " \"end_time\": \"1991-12-31\"}}" ] }, { "cell_type": "code", "execution_count": 5, "id": "00b0e839-816a-4d0b-88f0-146ce99baf7a", "metadata": {}, "outputs": [], "source": [ "# dictionary of variable names and time for mom5 and mom6\n", "vars_args = {\n", " \"mom5\": {\n", " \"var_temp\": 'temp',\n", " \"var_salt\": 'salt',\n", " \"var_area\": 'area_t'},\n", " \"mom6\": {\n", " \"var_temp\": 'thetao',\n", " \"var_salt\": 'so',\n", " \"var_area\": 'areacello'}}" ] }, { "cell_type": "markdown", "id": "06f4a900-f686-4e89-b27c-8e2c1b65895f", "metadata": {}, "source": [ "We define below a few functions used to load output (temperature or salinity) and compute the histogram for the T-S diagram." ] }, { "cell_type": "code", "execution_count": 6, "id": "cc5096c5-6c38-47ae-8379-6ad2f20aa977", "metadata": {}, "outputs": [], "source": [ "def gsw_SA_from_SP(salt, lon_name):\n", " \"\"\"function to convert practical salinity to absolute salinity\n", " using the Gibbs SeaWater (GSW) Oceanographic Toolbox of TEOS-10\n", " (https://teos-10.github.io/GSW-Python/)\n", " \n", " input:\n", " -salt: practical salinity (array)\n", " -lon_name: name of the longitude (str)\n", " output:\n", " -salt_abs: absolute salinity (array)\n", " \"\"\"\n", " pres = gsw.p_from_z(-salt.cf['vertical'], salt.cf['latitude'])\n", " salt_abs = gsw.SA_from_SP(salt, pres, salt[lon_name], salt.cf['latitude'])\n", " salt_abs.attrs = {'units': 'Absolute Salinity (g/kg)'}\n", " return salt_abs" ] }, { "cell_type": "code", "execution_count": 7, "id": "9be5f1c6-de68-463d-971b-90aec3bf70f8", "metadata": {}, "outputs": [], "source": [ "# a function to load salinity\n", "def load_salinity(model):\n", " salt = cc.querying.getvar(\n", " variable=vars_args[model].get('var_salt'), session=session, frequency='1 monthly',\n", " **expt_args[model])\n", " lon_name = salt.cf['longitude'].name\n", " # select longitude, latitude range and time\n", " salt = salt.cf.sel(longitude=lon, method='nearest').cf.sel(\n", " latitude=lat, time=slice(expt_args[model].get('start_time'),\n", " expt_args[model].get('end_time')))\n", " # convert from practical to absolute salinity\n", " salt = gsw_SA_from_SP(salt, lon_name)\n", " return salt" ] }, { "cell_type": "code", "execution_count": 8, "id": "1443f5e3-c496-440a-a9c3-0334e30e6285", "metadata": {}, "outputs": [], "source": [ "# a function to load temperature\n", "def load_temperature(model):\n", " temp = cc.querying.getvar(\n", " variable=vars_args[model].get('var_temp'), session=session, frequency='1 monthly',\n", " **expt_args[model])\n", " # select longitude, latitude range and time\n", " temp = temp.cf.sel(longitude=lon, method='nearest').cf.sel(\n", " latitude=lat, time=slice(expt_args[model].get('start_time'),\n", " expt_args[model].get('end_time')))\n", " if model == 'mom5':\n", " temp = temp - 273.15\n", " elif model == 'mom6':\n", " # convert potential to conservative temperature\n", " temp = gsw.conversions.CT_from_pt(salt, temp)\n", " temp.name = 'temp'\n", " temp.attrs = {'units': 'Conservative temperature (°C)'}\n", " return temp" ] }, { "cell_type": "code", "execution_count": 9, "id": "39196ea9-a681-4f8f-a098-ce24995b2747", "metadata": {}, "outputs": [], "source": [ "# a function to load area of grid cells\n", "def load_grid_areas(model):\n", " area = cc.querying.getvar(\n", " variable=vars_args[model].get('var_area'), session=session, n=1,\n", " **expt_args[model])\n", " if model == 'mom5':\n", " area = area.drop(['geolon_t', 'geolat_t'])\n", " # select longitude and latitude range\n", " area = area.cf.sel(longitude=lon, method='nearest').cf.sel(\n", " latitude=lat)\n", " return area" ] }, { "cell_type": "code", "execution_count": 10, "id": "35ff62fa-345b-4202-948c-cdc0bf283d67", "metadata": {}, "outputs": [], "source": [ "# a function that computes the temperature and salinity bins for 2D histogram \n", "def compute_TS_bins(salt, temp, area):\n", " temp_bins = np.arange(np.floor(temp.min().values), np.ceil(temp.max().values), 0.5)\n", " salt_bins = np.arange(np.floor(salt.min().values), np.ceil(salt.max().values), 0.1)\n", " # for density contours in TS diagram\n", " temp_bins_mesh,salt_bins_mesh = np.meshgrid(temp_bins, salt_bins)\n", " TS_density = gsw.density.sigma2(salt_bins_mesh, temp_bins_mesh)\n", " # volume of grid cells to account for varying grid cells especially in the vertical\n", " vol = (temp.cf['vertical'] * area)\n", "\n", " # 2D histogram of temperature and salinity weighted by volume\n", " TS_hist = xhistogram(\n", " temp, salt, bins=(temp_bins, salt_bins), weights=vol)\n", " TS_hist = TS_hist.where(TS_hist != 0).compute()\n", " return TS_hist, TS_density, salt_bins_mesh, temp_bins_mesh " ] }, { "cell_type": "markdown", "id": "30795fa8-c8bd-47ad-9662-dff5d5af409b", "metadata": {}, "source": [ "Now let's select a model, load everything, and compute the TS diagram" ] }, { "cell_type": "markdown", "id": "6e14f450-8783-4fa1-8c98-d17863508b00", "metadata": {}, "source": [ "## MOM5" ] }, { "cell_type": "code", "execution_count": 11, "id": "1691b512-91b7-4cca-860d-d117a0cb2ea1", "metadata": {}, "outputs": [], "source": [ "model = 'mom5' # 'mom5' or 'mom6'\n", "salt = load_salinity(model)\n", "temp = load_temperature(model)\n", "area = load_grid_areas(model)" ] }, { "cell_type": "markdown", "id": "bf810deb-8466-44aa-a485-5188e35a9fd0", "metadata": {}, "source": [ "### Calculate and plot the TS diagram" ] }, { "cell_type": "code", "execution_count": 12, "id": "c96bfc08-02b1-468f-9096-9dea269c40cc", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:611: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:611: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:611: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:611: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:611: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:611: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:611: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:640: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:640: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:640: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:640: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:640: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:640: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:640: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)\n" ] } ], "source": [ "TS_hist, TS_density, salt_bins_mesh, temp_bins_mesh = compute_TS_bins(salt, temp, area)" ] }, { "cell_type": "code", "execution_count": 13, "id": "013ee25c-6bf7-4d4a-a4e7-1eb4265b8a62", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 8))\n", "# logarithmic colormap to show both TS bins with smaller and\n", "# larger volumes (e.g. NADW, AABW)\n", "norm=colors.LogNorm(\n", " vmin=TS_hist.min().values, vmax=TS_hist.max().values)\n", "# volume weighted histogram of T and S\n", "TS_hist.plot(cmap=cmo.deep, norm=norm,\n", " cbar_kwargs=dict(label='volume ($m^{3}$)'))\n", "# density contours\n", "cs = plt.contour(\n", " salt_bins_mesh, temp_bins_mesh, TS_density, colors='silver',\n", " levels=np.arange(np.floor(TS_density.min()),\n", " np.ceil(TS_density.max()), .5))\n", "plt.clabel(cs, inline=True)\n", "\n", "plt.xlabel(salt.units)\n", "plt.ylabel(temp.units)\n", "plt.title(\"T-S with \"+model+\" ouput\");" ] }, { "cell_type": "markdown", "id": "a16ff888-6433-4034-bdbb-defa01f2c347", "metadata": {}, "source": [ "## MOM6" ] }, { "cell_type": "markdown", "id": "a6b20346-bcfa-45e7-b07e-eb91c4a93c50", "metadata": {}, "source": [ "Now let's do the same but selecting `mom6` as our model." ] }, { "cell_type": "code", "execution_count": 14, "id": "cccf2b9d-e200-4255-a67d-1a5bf5aab589", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:611: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:611: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:611: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:640: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:640: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:640: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:611: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:611: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:611: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmin(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:640: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:640: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)\n", "/g/data/hh5/public/apps/miniconda3/envs/analysis3-22.07/lib/python3.9/site-packages/dask/array/reductions.py:640: RuntimeWarning: All-NaN slice encountered\n", " return np.nanmax(x_chunk, axis=axis, keepdims=keepdims)\n" ] } ], "source": [ "model = 'mom6' # 'mom5' or 'mom6'\n", "salt = load_salinity(model)\n", "temp = load_temperature(model)\n", "area = load_grid_areas(model)\n", "TS_hist, TS_density, salt_bins_mesh, temp_bins_mesh = compute_TS_bins(salt, temp, area)" ] }, { "cell_type": "code", "execution_count": 15, "id": "c034281c-4a05-43f2-bec1-3e25bb766eed", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 8))\n", "# logarithmic colormap to show both TS bins with smaller and\n", "# larger volumes (e.g. NADW, AABW)\n", "norm=colors.LogNorm(\n", " vmin=TS_hist.min().values, vmax=TS_hist.max().values)\n", "# volume weighted histogram of T and S\n", "TS_hist.plot(cmap=cmo.deep, norm=norm,\n", " cbar_kwargs=dict(label='volume ($m^{3}$)'))\n", "# density contours\n", "cs = plt.contour(\n", " salt_bins_mesh, temp_bins_mesh, TS_density, colors='silver',\n", " levels=np.arange(np.floor(TS_density.min()),\n", " np.ceil(TS_density.max()), .5))\n", "plt.clabel(cs, inline=True)\n", "\n", "plt.xlabel(salt.units)\n", "plt.ylabel(temp.units)\n", "plt.title(\"T-S with \"+model+\" ouput\");" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:analysis3]", "language": "python", "name": "conda-env-analysis3-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.15" } }, "nbformat": 4, "nbformat_minor": 5 }